Unsupervised Learning Through Generalized Mixture Model
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Abstract A generalized way of building mixture models using different distributions is explored in this article. The EM algorithm is used with some modifications to accommodate different distributions within the same model. The model uses any point estimate available for the respective distributions to estimate the mixture components and model parameters. The study is focused on the application of mixture models in unsupervised learning problems, especially cluster analysis. The convenience of building mixture models using the generalized approach is further emphasised by appropriate examples, exploiting the well-known maximum likelihood and Bayesian estimates of the parameters of the parent distributions.
2019 ◽
Vol 2019
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pp. 1-10
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2021 ◽
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2012 ◽
Vol 2012
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pp. 1-5
2007 ◽
Vol 51
(12)
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pp. 6614-6623
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2021 ◽
Vol 8
(9)
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pp. 275-277
2012 ◽
Vol 532-533
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pp. 1445-1449
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